Post-approval confirmatory / supplementary data: registries and - - PowerPoint PPT Presentation

15th of December 2017 | Vincent Ho, Netherlands Cancer Registry (NCR) Netherlands Comprehensive Cancer Organisation (IKNL)

A potential role for ‘comprehensive’ cancer registries

Post-approval confirmatory / supplementary data: registries and observational trials

(potential) conflicts of interests None Sources of conflicts of interest may include (but are not limited to):

• Research funding
• Fees for presentations / publications
• Sponsoring

Disclosure

Postapproval data

• General motivation
• providing stakeholders information on the continued safety and

effectiveness of drugs, medical devices etc.

• extension beyond experimental context (controlled conditions,

selected patient populations, limited time horizons)

• Particularly
• expedited / conditional approval allowing manufacturers to

address unresolved issues (optimal dosing, long-term side effects, use in specific subgroups)

NEJM, September 2017

JNCI, 2011

• accelerated approval to 35 products for 47 new indications
• clinical benefit confirmed for 26/47 (conversion to regular approval)
• median time 3.9 years (range 0.8–12.6 years) for conversion
• confirmatory trials not completed for 14 indications

“The slow, irregular pace of postapproval studies contrasts starkly with the short, rigid deadlines and other shortcuts used to speed marketing approval” “The “catch 22” is that we only know the true performance of a product after approval, but the product must be safe and effective in order to be approved” (Muni, 2005)

BMJ, September 2017

Postapproval data

• Data collection methods?
• randomized, double-blinded, controlled trial
• randomized, unblinded clinical study
• observational (conditions of approval) studies
• non-randomized registry study with formal follow-up and data

collection (single arm trial)

• informal registry study (“open enrollment”) with less stringent

follow-up and data collection

• meta-analyses
• model/laboratory studies

Postapproval data

Which methods are best for collecting and analyzing postapproval data?

• Potentially, a large array of methodologies may transfer information on

a product’s performance in the ‘real world’, provided that this information is understood in the proper context

• No single method can meet all of the needs of stakeholders
• problem remains (and is perhaps even amplified):

when do we consider evidence compelling enough?

Postapproval data

• practical considerations do foster preferences for some methods over
• thers, particularly in case of rare instances:

(Grahame- Smith and Aronson, 2004)

Observational trials?

• Well-known caveats in methodology

(although sophisticated methods have emerged)

• Especially for rare conditions
• low number of cases in clinical practices
• representative samples (expertise across hospitals)?
• need for adequate screening platforms to direct patients to the

right doctor

(Clinical) registries

• Existing infrastructure with ‘real-world’ focus (population-based)
• Potential for flexible and adaptable data collection: retrospective

and prospective data on a variety of different parameters

• Standardisation of longitudinal data collection with the capability

to evolve (e.g. as more is learned about a given topic)

• Opportunities for linkage with other databases
• Rare conditions may be captured ‘along the way’
• Issues include:
• (generally) voluntary on the part of doctors and patients
• data quality and completeness

A role for cancer registries?

• Normally
• person characteristics (date of birth, age at diagnose, date of death)
• disease characteristics (topography, morphology)
• Sometimes
• stage of disease
• treatment (1st line)
• cause of death
• Rarely
• hormone receptors
• comorbidity
• recurrence, disease progression
• Hardly ever
• genetic profile
• 2nd and 3rd line treatment

(Kraywinkel, 2017)

Examples from the NCR

Cancer of unknown primary (CUP): incidence

Examples from the NCR

Cancer of unknown primary (CUP): first visit

Examples from the NCR

Cancer of unknown primary (CUP): first treatment

Examples from the NCR

Breast cancer receptors: ER, PR, HER2

Examples from the NCR

Breast cancer receptors: HER2 detection method

Examples from the NCR

Breast cancer receptors: relation with recurrence

(Kwast et al, 2011)

Examples from the NCR

• GIST: incidence

Examples from the NCR

• GIST: first line targeted therapy (blue)

Examples from the NCR

• GIST: first line imatinib (pink; in registry since 2014)

Examples from the NCR

• GIST: additional data collection as of 2016
• immunohistochemistry

CD117 DOG1 SDHB

• mutations

BRAF PDGF SDH

Examples from the NCR

• Glioblastoma: ‘real world’ confirmation of Stupp-trial

(Ho et al, 2014)

5 10 15 20 25 30 35 40 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 CCR*

Months Survival probability

0.0 0.25 0.5 0.75 1.0 6 12 18 24 30 36

The phase 3 AZA-001 trial1 Dutch daily practice (PHAROS MDS)2 Months

16.9 months Difference: 9.5 months Difference: 9.6 months BSC§

§BSC, best supportive care

MDS, myelodysplastic syndromes; *CCR, conventional care regimens (includes best supportive care, low-dose cytarabine and intensive chemotherapy)

24.5 months 15.0 months 7.3 months

2Dinmohamed AG et al. Leukemia. 29:2449-51 (2015) 1Fenaux P et al. Lancet Oncol. 10: 223-32 (2009)

Examples from the NCR

Examples from the NCR

Therapeutic effectiveness of novel, expensive agents in daily practice Retrospective studies:

• Azacitidine: MDS patients (2008–2011)
• Ibrutinib: ibrutinib-treated CLL patients (2015–2016)
• Brenduximab vendotin: brentuximab vendotin-treated HL patients

(2015–2016)

• Nivolumab: nivolumab-treated HL-patients (2016–2018)

Prospective studies:

• Pomalidomide: MM patients (2015– ; pay-for-performance)
• Daratumumab: MM patients (2018– ; pay-for-performance)

‘Comprehensive’ cancer registry?

• National database since 1989
• coverage estimated at 95%
• > 2 million cases in database
• > 100.000 cases per year
• Flexible registry…

PLCRC

Patient population: Colorectal carcinoma (all stages) Observational study: Collection of data Interventional study: Cohort Multiple Randomised Controlled Trial (cmRCT) Clinical Tissue Blood PROMs

(Relton et al, 2010)

Summary

• Data for postapproval evaluation of agents may be hard to come by.
• Most postapproval studies have yet to confirm preliminary results

used to substantiate initial approval.

• Cancer registries may aid in collecting impactful data on well-defined
• utcomes of interest in postapproval evaluation and observatonial

studies.

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Process

LBZ PALGA

• ther

databases NCR